A Cognitive Interpretation of Prompt Refinement and Iterative Feedback in Generative AI

OGNR Prompt Cognition White Paper Vol. 1 (2025) A Cognitive Interpretation of Prompt Refinement and Iterative Feedback in Generative AI Shim, Jae-Hong (OGNR) — October 2025

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Abstract

This thesis-type white paper reinterprets the human prompt refinement and iterative feedback processes in a generative AI environment from the perspectives of cognitive alignment and convergence. The iterative process of a user revising and re-entering a prompt and evaluating the AI ​​output is not a simple command; it is an interaction between human intention and judgment and the model's knowledge structure. This paper presents the Prompt Cognition Framework proposed by OGNR and discusses (1) the functional differences between short and structured prompts, (2) the drivers and effects of the iterative feedback loop, and (3) the directions for academic and industrial applications. 1. Introduction

1.1 Research Background

Generative AI uses natural language to produce output, and the user's prompt design directly influences the model's output. Prompt refinement is not simply a technical process of adjusting input; it is an interactive process that externalizes human thinking and stimulates the AI's knowledge network.

1.2 Problem Statement

Recent prompt engineering research has focused on improving the accuracy and performance of LLM. However, theoretical considerations regarding the cognitive structure of prompt refinement remain insufficient.

1.3 Research Objectives

1. Analyze the cognitive structure of prompt refinement and iterative feedback.

2. Compare the functional advantages and disadvantages of short-form versus structured prompts.

3. Model the impact of iterative interaction on output quality.

4. Suggest practical applications in education, design, and video creation.

5. Verify consistency with prior literature and clarify differences.

2. Related Work

2.1 Human-in-the-Loop (HITL)

HITL encompasses methods for designing human intervention into machine learning systems. Wu et al. (2021), Wang et al. (2022) et al. conducted related research.

2.2 Prompt Engineering Research

Sahoo et al. (2024), Schulhoff et al. (2024), and Gu et al. (2023) systematized prompt design and techniques.

2.3 Human-AI Co-creation and Interaction Research

Research on human-AI co-creation considers AI as a collaborator in creative activities, and reports that human intervention, feedback, and iterative adjustments contribute to improving the quality of the work. 3. Prompt Cognition Framework

3.1 Concepts and Components

• Intent: The user's goal

• Expression: The initial prompt

• Generation: The model response

• Reflection: The response evaluation

• Refinement: The prompt adjustment

3.2 Short vs. Structured Prompts

Distinction: Short vs. Structured

Focus: Simple instructions, field-based structure

Advantages: Rapid experimentation, ensuring consistency

Limitations: Reduced variability and reproducibility, reduced freedom of expression

3.3 Iterative Feedback Loop

• User feedback, modification strategies, and model response changes

• Increased output consistency, stable pattern extraction, and user learning

3.4 Framework Extension

1st: Word/Sentence Refinement, 2nd: Structure Refinement, 3rd: Strategy Refinement

4. Empirical Observations & Hypothesis Propositions

• Short: Excellent diversity and unpredictability

• Structured: Stable consistency and repeating patterns

• Iterative Refinement Loop: 3-5 rounds → Similar cases to target responses Increase

Hypothesis

H1: Positive correlation between number of iterative refinements and response relevance. H2: Short sentences = explorability, structured sentences = consistency. H3: Diminishing returns are felt when initial prompt quality exceeds a certain level. H4: Hybrid strategies provide balanced performance.

5. Positioning, Differentiation & Verifiability

• Differentiation from existing HITL/prompt research: Focused on cognitive alignment and iterative convergence structure.

• Verification: Quantitative experiments, user experiments, tool-based log analysis.

6. Application & Implication

• Education: Prompt literacy training module.

• Creation, design, and VFX: Internalization of work workflows.

• Research and industry: R&D projects, log dataset construction.

7. Conclusion

This study reinterprets the iterative refinement and feedback process of prompts from the perspective of cognitive alignment and convergence, and proposes the Prompt Cognition Framework. The iterative loop, functional differences between prompt types, and applicability will be examined. ________________________________________

References 1. Wu et al., “A Survey of Human-in-the-loop for Machine Learning”, ResearchGate, 2021 2. Wang, Gu & Chen, “Human-in-the-loop in Machine Learning Lifecycle”, arXiv:2202.10564, 2022 3. Sahoo et al., “Prompt Engineering Review”, arXiv:2402.07927, 2024 4. Schulhoff et al., “The Prompt Report”, arXiv:2406.06608, 2024 5. Gu et al., “Vision-Language Prompt Engineering”, arXiv:2307.12980, 2023